Group Importances#
In this notebook we show how to compute and interpret Overall Importances shown in InterpretML’s Global Explanations for EBMs. We also show how to compute importances of a group of features or terms.
Throughout the notebook we use term to denote both single features and interactions (pairs).
This notebook can be found in our examples folder on GitHub.
# install interpret if not already installed
try:
import interpret
except ModuleNotFoundError:
!pip install --quiet interpret pandas scikit-learn
Train an Explainable Boosting Machine (EBM) for a regression task
Let’s use the Boston dataset as a reference and train an EBM.
import numpy as np
import pandas as pd
from sklearn.datasets import load_diabetes
from interpret.glassbox import ExplainableBoostingRegressor
from interpret import set_visualize_provider
from interpret.provider import InlineProvider
set_visualize_provider(InlineProvider())
X, y = load_diabetes(return_X_y=True, as_frame=True)
ebm = ExplainableBoostingRegressor()
ebm.fit(X, y)
ExplainableBoostingRegressor()In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.
ExplainableBoostingRegressor()
Explain the Model
EBMs provide two different kinds of explanations: global explanations about the overall model behavior and local explanations about individual predictions from the model.
Global Explanation
Global Explanations are useful for understanding what a model finds important, as well as identifying potential flaws in its decision making or the training data. Let’s start by computing and displaying a global explanation:
from interpret import show
show(ebm.explain_global(name='EBM'))
The overall importance for each term is calculated as the average absolute contribution (score) a term (feature or pair) makes when predicting across the training dataset. This way of measuring term importance tends to favor terms which, on average, have large impact on predictions for many cases. The overall importance is not a measure of positive/negative – it is a measure of how important each term is in the scores. For regression, these scores are represented in the same units as the y-axis of the feature graphs. For classification, the scores would be in logits.
Going beyond overall term importances, because EBMs are additive models we can measure exactly how each term contributes to a prediction. Let’s take a look at the graph of the term, bp, by selecting it in the drop-down menu.
The way to interpret this is that if a new datapoint came in with bp = 0.1, the model adds about +33.1 to the final prediction. However, for a different datapoint with bp = 0.13, the model would now add approx. +36.7 to the prediction.
To make individual predictions, the model uses each term graph as a look up table, notes the contribution per term, and sums them together with the learned intercept to make a prediction. In regression, the intercept is the mean target (label) of the training set, and each term adds or subtracts to this mean. In classification, the intercept reflects the base rate of the positive class on a log scale. The gray above and below the graph shows the confidence of the model in that region of the graph.
Local Explanations
We can see the full breakdown of a prediction on a single sample with Local Explanations. Here’s how to compute the prediction breakdown for the first sample in our dataset:
from interpret import show
show(ebm.explain_local(X[:1], y[:1]), 0)
Let’s take a look at the prediction by selecting it in the drop-down menu.
The model prediction is 188.50. We can see that the intercept adds about +151.9, bp subtracts about 0.02, and age adds about 0.04. If we repeat this process for all the terms, we’ll arrive exactly at the model prediction of 188.50.
Viewing _all_ term importances
Due to space limitations in our graphs, the term importance summary only shows the top 15 terms. To view the overall importances of all terms of a trained EBM - the scores shown in the global explanation summary - we use term_importances():
importances = ebm.term_importances()
names = ebm.term_names_
for (term_name, importance) in zip(names, importances):
print(f"Term {term_name} importance: {importance}")
Term age importance: 3.474951244886729
Term sex importance: 7.846076749279368
Term bmi importance: 16.645152779591363
Term bp importance: 10.440881306883494
Term s1 importance: 0.9013220293372795
Term s2 importance: 2.79635592330311
Term s3 importance: 7.180537892517861
Term s4 importance: 6.312991173866052
Term s5 importance: 16.168276230206757
Term s6 importance: 5.355217570284118
Term age & bmi importance: 0.8076576127421041
Term age & s5 importance: 1.328811243771473
Term bmi & bp importance: 1.136375440232964
Term bmi & s2 importance: 0.942714325514549
Term bmi & s4 importance: 1.4351834805610202
Term bmi & s5 importance: 0.8927158253198318
Term bmi & s6 importance: 0.9979454798265691
Term bp & s1 importance: 0.7359201556886985
Term s1 & s5 importance: 1.381229421359335
Term s5 & s6 importance: 2.0763454087292508
Note that mean absolute contribution isn’t the only way of calculating term importances. Another metric our package provides is the min_max option, which computes the difference between the max (the highest score on the graph) and min (the lowest score on the graph) values for each term. Term importance measured with min_max is a measure of the maximum impact a term can have, even though it might have this amount of impact on very few cases, whereas avg_weight(the default parameter) is a measure of typical (average) contribution of a term across all cases.
importances = ebm.term_importances("min_max")
names = ebm.term_names_
for (term, importance) in zip(names, importances):
print(f"Term {term} importance: {importance}")
Term age importance: 14.928418958594722
Term sex importance: 15.755380183433186
Term bmi importance: 91.99799458015363
Term bp importance: 64.47512673772853
Term s1 importance: 7.9162108716590165
Term s2 importance: 20.227229911362684
Term s3 importance: 50.89858962858089
Term s4 importance: 30.867257809651562
Term s5 importance: 57.999163750400584
Term s6 importance: 37.844564205154086
Term age & bmi importance: 10.467820767348574
Term age & s5 importance: 7.110198798813888
Term bmi & bp importance: 12.544783294027194
Term bmi & s2 importance: 12.62458448944908
Term bmi & s4 importance: 7.155264137432992
Term bmi & s5 importance: 6.800564026407826
Term bmi & s6 importance: 8.515585159080581
Term bp & s1 importance: 8.448042632189209
Term s1 & s5 importance: 15.86408336428848
Term s5 & s6 importance: 22.12673117125712
Feature/Term Group Importances
We provide utility functions to compute the importances of groups of features or terms and, optionally, append these importances to the global feature attribution bar graph. Note that shape function graphs are not generated for groups of features/terms, just their overall importance is shown on the Summary.
Grouping terms and then calculating and displaying their importance does not change the model and the predictions it makes in any way – group importances are just a method for computing the importance of groups of terms in addition to the importances of individual terms that are already calculated. As you’ll see in the examples below, it’s OK for features/terms to overlap in different groups.
Computing group importances
Let’s use the Adult dataset and train an EBM for a classification task.
import numpy as np
import pandas as pd
from interpret.glassbox import ExplainableBoostingClassifier
df = pd.read_csv(
"https://archive.ics.uci.edu/ml/machine-learning-databases/adult/adult.data",
header=None)
df.columns = [
"Age", "WorkClass", "fnlwgt", "Education", "EducationNum",
"MaritalStatus", "Occupation", "Relationship", "Race", "Gender",
"CapitalGain", "CapitalLoss", "HoursPerWeek", "NativeCountry", "Income"
]
X = df.iloc[:, :-1]
y = df.iloc[:, -1]
adult_ebm = ExplainableBoostingClassifier()
adult_ebm.fit(X, y)
ExplainableBoostingClassifier()In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
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ExplainableBoostingClassifier()
We then create a list of terms – single features or interactions – as our group and compute its importance:
from interpret.glassbox._ebm._research import *
social_feature_group = ["MaritalStatus", "Relationship", "Race", "Gender", "NativeCountry"]
importance = compute_group_importance(social_feature_group, adult_ebm, X)
print(f"Group: {social_feature_group} - Importance: {importance}")
Group: ['MaritalStatus', 'Relationship', 'Race', 'Gender', 'NativeCountry'] - Importance: 1.2936225991179775
In this example we create a group with five terms and compute its importance. Similar to single feature importances, we interpret this score as the average absolute contribution this group of terms makes when predicting across the training dataset. Note that for each prediction, the contribution of each term in the group will be added before taking the absolute value.
We also have the option to create a global explanation containing the group importance or append it to an existing explanation:
my_global_exp = append_group_importance(social_feature_group, adult_ebm, X)
show(my_global_exp)
The importance of social_feature_group is about 1.30, which is higher than the importance of any individual feature/term:
We could make this type of comparison between different groups too:
education_feature_group = ["Education", "EducationNum"]
relationship_feature_group = ["MaritalStatus", "Relationship"]
social_feature_group = ["MaritalStatus", "Relationship", "Race", "Gender", "NativeCountry"]
my_global_exp = append_group_importance(social_feature_group, adult_ebm, X)
my_global_exp = append_group_importance(education_feature_group, adult_ebm, X, global_exp=my_global_exp)
my_global_exp = append_group_importance(relationship_feature_group, adult_ebm, X, global_exp=my_global_exp)
show(my_global_exp)
The importance of education_feature_group is about 0.52, higher than each of its individual terms but smaller than some individual terms such as Age. Remember, creating groups of features/terms does not, in any way, change the model and its predictions, it only allows you to estimate the importance of these groups.
This graph, for example, suggests that features related to relationships are more important than features reated to education.
We can also compare a group we are interested in (e.g. social_feature_group) with a group of all other reamining terms.
social_feature_group = ["MaritalStatus", "Relationship", "Race", "Gender", "NativeCountry"]
all_other_terms = [term for term in adult_ebm.term_names_ if term not in social_feature_group]
my_global_exp = append_group_importance(social_feature_group, adult_ebm, X)
my_global_exp = append_group_importance(all_other_terms, adult_ebm, X, group_name="all_other_terms", global_exp=my_global_exp)
show(my_global_exp)
Note that all_other_terms has the highest importance score, followed by social_feature_group.
It’s even possible to create a group with all terms.
all_terms_group = [term for term in adult_ebm.term_names_]
mew_global_exp = append_group_importance(all_terms_group, adult_ebm, X, group_name="all_terms")
show(mew_global_exp)
Finally, we also expose a function to compute the importances of a group of terms as well as all the model’s original terms.
my_dict = get_group_and_individual_importances([social_feature_group, education_feature_group], adult_ebm, X)
for key in my_dict:
print(f"Term: {key} - Importance: {my_dict[key]}")
Term: MaritalStatus, Relationship, Race, Gender, NativeCountry - Importance: 1.2936225991179775
Term: Age - Importance: 0.8256421395264449
Term: CapitalGain - Importance: 0.6709160308864925
Term: Relationship - Importance: 0.5846817109893102
Term: MaritalStatus - Importance: 0.5612448944333843
Term: Education, EducationNum - Importance: 0.5251461148977613
Term: EducationNum - Importance: 0.39644173830058643
Term: Occupation - Importance: 0.3727313194353788
Term: Gender - Importance: 0.3172276027811038
Term: HoursPerWeek - Importance: 0.2942303542214279
Term: Education - Importance: 0.1713599922390786
Term: CapitalLoss - Importance: 0.16918337687532692
Term: fnlwgt - Importance: 0.11671647688180961
Term: WorkClass - Importance: 0.0926617708053803
Term: Age & HoursPerWeek - Importance: 0.0713847879168948
Term: Race - Importance: 0.0666845161311077
Term: NativeCountry - Importance: 0.0636647524399239
Term: MaritalStatus & HoursPerWeek - Importance: 0.05470336426790857
Term: Relationship & HoursPerWeek - Importance: 0.046064075899869626
Term: EducationNum & MaritalStatus - Importance: 0.04525309282074548
Term: Age & EducationNum - Importance: 0.04138498350510662
Term: Age & fnlwgt - Importance: 0.03627275097143037
Term: fnlwgt & Education - Importance: 0.034058824191979455
Term: Age & Occupation - Importance: 0.032405339804679925
Term: Gender & HoursPerWeek - Importance: 0.03212725187529936
Term: Age & Race - Importance: 0.028175542262938717
Term: Age & Relationship - Importance: 0.022171654258700838
Term: WorkClass & EducationNum - Importance: 0.021148006886964683
Term: WorkClass & Relationship - Importance: 0.020008577618553642
Term: WorkClass & Race - Importance: 0.008472160039925158